Genome-Wide Association Studies in Alzheimer's Disease

Introduction

Late-onset Alzheimer's disease (LOAD) is among the most frequently encountered diseases in aging societies. Approximately 5.5 million people in the United States and 17 million people worldwide suffer from the disease. By age 85 years and older, 15%–30% are affected and the incidence rate increases from approximately 1% among people aged 65–70 years to approximately 6%-8% for people aged 85 years and older.1, 2 It is expected that these numbers will quadruple by the year 2040 resulting in a considerable public health burden.3

Abstract

INTRODUCTION

Late-onset Alzheimer's disease (LOAD) is among the most common diseases in aging societies but the genetic factors contributing to the disease are largely unknown.

OBJECTIVE

To summarize the current knowledge on the genetic causes underlying LOAD and specifically review the recent discoveries, strengths, and pitfalls of genome-wide association studies.

METHODS

The primary sources of the studies addressed in this review article were the AlzGene database updated May 2010, and full-text articles and abstracts published in English in the PUBMED database between 1980 and October 2010.

RESULTS

Despite remarkable technological advances in gene mapping in recent years, to date only five genes—amyloid precursor protein (APP), presenilin 1 (PSEN1), presenilin 2 (PSEN2), apolipoprotein E (APOE), and the neuronal sortilin-related receptor gene (SORL1)—have been firmly implicated in the cause. Among the additional genes reported, in particular CLU, CR1, PICALM, and BIN1 are promising candidates as they are biologically plausible and have been identified by the largest GWAS data sets available to date.

CONCLUSION

High-throughput whole-genome association studies have gained considerable momentum for the identification of novel disease genetic variants for LOAD risk genes, but no single functional risk variant was identified. For the promising candidate genes reported, both replication and validation by a sufficient number of independent data sets and functional approaches are still lacking but crucial.

Keywords

Alzheimer's disease, genetics, gene, genome-wide association studies